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Title: Swiss-chocolate : sentiment detection using sparse SVMs and part-of-speech n-grams
Authors : Jaggi, Martin
Uzdilli, Fatih
Cieliebak, Mark
Proceedings: Proceedings of the International Workshop on Semantic Evaluation (SemEval-2014)
Pages : 601
Pages to: 604
Conference details: International Workshop on Semantic Evaluation (SemEval-2014), Dublin, August 23-24 2014
Publisher / Ed. Institution : Association for Computational Linguistics
Issue Date: 2014
License (according to publishing contract) : CC BY 4.0: Namensnennung 4.0 International
Type of review: Not specified
Language : English
Subjects : Support vector machine; Classifier; Sentiment analysis
Subject (DDC) : 410.285: Computational linguistics
Abstract: We describe a classifier to predict the message-level sentiment of English microblog messages from Twitter. This paper describes the classifier submitted to the SemEval-2014 competition (Task 9B). Our approach was to build up on the system of the last year’s winning approach by NRC Canada 2013 (Mohammad et al., 2013), with some modifications and additions of features, and additional sentiment lexicons. Furthermore, we used a sparse (l1-regularized) SVM, instead of the more commonly used l2-regularization, resulting in a very sparse linear classifier.
Departement: School of Engineering
Organisational Unit: Institute of Applied Information Technology (InIT)
Publication type: Conference Paper
DOI : 10.21256/zhaw-3780
ISBN: 978-1-63266-621-5
Appears in Collections:Publikationen School of Engineering

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